def show_spiral():
x, t = spiral.load_data()
#print(x.shape, t.shape)
plt.scatter(x[0:100, 0], x[0:100, 1])
plt.scatter(x[100:200, 0], x[100:200, 1])
plt.scatter(x[200:300, 0], x[200:300, 1])
plt.show()
def show_learning_spiral(model, loss_list, x):
# 学習曲線
#plt.plot(loss_list)
#plt.show()
# 境界領域のプロット
h = 0.001
x_min, x_max = x[:, 0].min() - .1, x[:, 0].max() + .1
y_min, y_max = x[:, 1].min() - .1, x[:, 1].max() + .1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
X = np.c_[xx.ravel(), yy.ravel()]
score = model.predict(X)
predict_cls = np.argmax(score, axis=1)
Z = predict_cls.reshape(xx.shape)
plt.contourf(xx, yy, Z)
plt.axis('off')
# データ点のプロット
x, t = spiral.load_data()
N = 100
CLS_NUM = 3
markers = ['o', 'x', '^']
for i in range(CLS_NUM):
plt.scatter(x[i * N:(i + 1) * N, 0],
x[i * N:(i + 1) * N, 1],
s=40,
marker=markers[i])
plt.show()
def main():
max_epoch = 300
batch_size = 30
hidden_size = 10
learning_rate = 1.0
x, t = spiral.load_data()
model = TwoLayerNet(input_size=2, hidden_size=hidden_size, output_size=3)
optimizer = SGD(lr=learning_rate)
trainer = Trainer(model, optimizer)
trainer.fit(x, t, max_epoch, batch_size, eval_interval=10)
def main():
max_epoch = 300
batch_size = 30
hidden_size = 10
output_size = 3
learning_rate = 1.0
x, t = spiral.load_data()
model = TwoLayerNet(2, hidden_size, output_size)
optimizer = SGD(learning_rate)
trainer = Trainer(model, optimizer)
trainer.fit(x, t, max_epoch, batch_size)
trainer.plot()
def spiral_prot():
x, t = spiral.load_data()
print(f"x \n{x.shape}")
print(f"t \n{t.shape}")
# データ点のプロット
N = 100
CLS_NUM = 3
markers = ["o", "x", "^"]
for i in range(CLS_NUM):
# 散布図のプロット
plt.scatter(x[i*N:(i+1)*N, 0], x[i*N:(i+1)*N, 1], s=40, marker=markers[i])
plt.show()
import sys
sys.path.append('..') # 부모 디렉터리의 파일을 가져올 수 있도록 설정
from dataset import spiral
import matplotlib.pyplot as plt
x, t = spiral.load_data()
print('x', x.shape) # (300, 2)
print('t', t.shape) # (300, 3)
# 데이터점 플롯
N = 100
CLS_NUM = 3
markers = ['o', 'x', '^']
for i in range(CLS_NUM):
plt.scatter(x[i * N:(i + 1) * N, 0],
x[i * N:(i + 1) * N, 1],
s=40,
marker=markers[i])
plt.show()
import sys
sys.path.append('..') # 부모 디렉터리의 파일을 가져올 수 있도록 설정
import numpy as np
from common.optimizer import SGD
from dataset import spiral
import matplotlib.pyplot as plt
from two_layer_net import TwoLayerNet
# 하이퍼파라미터 설정
max_epoch = 300
batch_size = 30
hidden_size = 10
learning_rate = 1.0
# 데이터 읽기, 모델과 옵티마이저 생성
x, t = spiral.load_data(
) # (300,2), (300,3) : 300개의 학습데이터가 하나의 배치당 두개의 노드로 구성, t는 3개의 class 정답레이블(one_hot)
#print(f'x.shape:{x.shape}, type(x):{type(x)}\nt.shape:{t.shape}, type(t):{type(t)}')
model = TwoLayerNet(input_size=2, hidden_size=hidden_size, output_size=3)
optimizer = SGD(lr=learning_rate)
# 학습에 사용하는 변수
data_size = len(x)
max_iters = data_size // batch_size #소수점 버림.
#print(data_size, batch_size, max_iters, 11//3);exit(1)
total_loss = 0
loss_count = 0
loss_list = []
for epoch in range(max_epoch):
# 데이터 뒤섞기
idx = np.random.permutation(data_size) #0~data_size-1 숫자를 랜덤으로 섞어줌.
b = np.array([4, 5, 6])
print(np.dot(a, b))
# 行列の積
A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])
print(np.dot(A, B))
def all_connection():
W1 = np.random.randn(2, 4)
b1 = np.random.randn(4)
W2 = np.random.randn(4, 3)
b2 = np.random.randn(3)
x = np.random.randn(10, 2) # 10個のサンプルデータ
h = np.dot(x, W1) + b1
#print(W1)
#print(b1)
#print(x)
print(h)
a = Sigmoid.forward(h)
s = np.dot(a, W2) + b2
print(s)
if __name__ == "__main__":
#show_sigmoid()
#show_spiral()
#all_connection()
x, t = spiral.load_data(4343)
model, loss_list = train_custom_loop(x, t)
show_learning_spiral(model, loss_list, x)
def main():
# (1) ハイパーパラメータの設定
max_epoch = 300
batch_size = 30
hidden_size = 10
learning_rate = 1.0
# (2) データの読み込み、モデルとオプティマイザの生成
x, t = spiral.load_data()
model = TwoLayerNet(input_size=2, hidden_size=hidden_size, output_size=3)
optimizer = SGD(lr=learning_rate)
# 学習で使用する変数
data_size = len(x)
max_iters = data_size // batch_size
total_loss = 0
loss_count = 0
loss_list = []
for epoch in range(max_epoch):
# (3) データのシャッフル
idx = np.random.permutation(data_size)
x = x[idx]
t = t[idx]
for iters in range(max_iters):
batch_x = x[iters*batch_size:(iters+1)*batch_size]
batch_t = t[iters*batch_size:(iters+1)*batch_size]
# (4) 勾配を求め、パラメータを更新
loss = model.forward(batch_x, batch_t)
model.backward()
optimizer.update(model.params, model.grads)
total_loss += loss
loss_count += 1
# (5) 定期的に学習経過を出力
if (iters + 1) % 10 == 0:
avg_loss = total_loss / loss_count
print(f"| epoch {epoch+1} | iter {iters+1} / {max_iters} | loss {avg_loss}")
loss_list.append(avg_loss)
total_loss, loss_count = 0, 0
print(f"loss_list: \n{loss_list}")
# 学習結果のプロット
plt.plot(np.arange(len(loss_list)), np.asarray(loss_list))
plt.xlabel('iterations (x10)')
plt.ylabel('loss')
plt.show()
# 境界領域のプロット
h = 0.001
x_min, x_max = x[:, 0].min() - 0.1, x[:, 0].max() + 0.1
y_min, y_max = x[:, 1].min() - 0.1, x[:, 1].max() + 0.1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
X = np.c_[xx.ravel(), yy.ravel()]
score = model.predict(X)
predict_cls = np.argmax(score, axis=1)
Z = predict_cls.reshape(xx.shape)
plt.contourf(xx, yy, Z)
plt.axis('off')
# データ点のプロット
x, t = spiral.load_data()
N = 100
CLS_NUM = 3
markers = ['o', 'x', '^']
for i in range(CLS_NUM):
plt.scatter(x[i*N:(i+1)*N, 0], x[i*N:(i+1)*N, 1], s=40, marker=markers[i])
plt.show()
sys.path.append('..') # 親ディレクトリのファイルをインポートするための設定
import numpy as np
from common.optimizer import SGD
from dataset import spiral
import matplotlib.pyplot as plt
from two_layer_net import TwoLayerNet
# ハイパーパラメータの設定
max_epoch = 300
batch_size = 30
hidden_size = 10
learning_rate = 1.0
# もしload_data()の引数ランダムシードを与えるならのちのload_data()も同じにすべき。
x, t = spiral.load_data() # 300個の座標らと、300個のone-hotな正解ら。
# 入力層のニューロンは2個、唯一の隠れ層のニューロンは10個、出力層のニューロンは3個。
model = TwoLayerNet(input_size=2, hidden_size=hidden_size, output_size=3)
optimizer = SGD(lr=learning_rate) # 学習率は1である。stochastic gradient descent.
# 学習で使用する変数
data_size = len(x) # 300
max_iters = data_size // batch_size # 300/30==10
total_loss = 0
loss_count = 0
loss_list = []
for epoch in range(max_epoch):
# データのシャッフル
idx = np.random.permutation(data_size)
x = x[idx]
